Consistency in image rendering helps to allow a more accurate clinical evaluation when using x-rays and related types of diagnostic images. Images taken of the same anatomy that have the same overall dynamic range and contrast settings can be more readily compared against one another for diagnosis and for tracking various conditions, particularly for images taken of the same patient at different times and on different imaging apparatus. In an Intensive Care Unit (ICU), for example, a succession of diagnostic images taken over a time period may help to show the progress of a patient's condition and help to direct ICU treatment accordingly.
In practice, however, consistent image rendering has proved difficult to achieve. Differences in image quality from one image to the next can be significant, owing to differences in exposure settings, patient and apparatus positioning, scattering, and grid application, for example. Thus, even for images obtained from the same patient over a short treatment interval, there can be substantial differences between two or more images that prevent effective comparison between them and constrain the ability of the clinician to detect subtle changes that can be highly significant. This problem relates to images whether originally obtained on film and scanned, or digitally obtained, such as using a computed radiography (CR) or digital radiography (DR) system.
Computed radiography systems that use storage phosphors and digital radiography systems can offer a very wide exposure latitude (as much as 10,000:1) compared with that available from conventional screen/film systems (typically 40:1). This means that exposure error is much less serious for computed radiography at the time of image sensing and recording. However, image display apparatus have a much more limited dynamic range. Tone scale mapping in computed radiography can be specifically tailored to provide an optimal rendition of every individual image. However, most output media, such as photographic film and displays such as flat-panel or cathode ray tube (CRT) displays do not have wide enough dynamic range to display this information at nearly 10,000:1 latitude with proper visual contrast. It is, therefore, necessary to carefully allocate the available output dynamic range to display the clinically important part of the input code values.
For some applications, the range of the region of interest in the input image may exceed that provided by the output media or display, and the contrast of parts of the input image may then be compromised as a result. For example, U.S. Pat. No. 4,302,672 entitled “Image Gradation Processing Method And Apparatus For Radiation Image Recording System” to Kato et al. teaches a method of constructing such a compromised tone-scale curve for chest x-ray images. However, that method uses the valleys and peaks of the code-value histogram to identify the critical points between the spine, the heart, and the lung. The results are not very reliable because these valleys and peaks are not always clearly detectable. This method requires that all images obtained have the same overall spatial profile, which need not be true. Furthermore, the method cannot be generalized to examinations other than chest images.
From one perspective, there are chiefly five classes of “objects” in radiographic images: (1) foreground (collimator blades used to protect parts of the body from unnecessary x-ray exposure) usually corresponding to very low to low exposure areas; (2) man-made objects (such as pacemakers, tubes, and electrodes); (3) soft tissues (such as muscles, blood vessels, and intestines) usually corresponding to low (e.g., mediastinum) to high (e.g., lung) exposures depending on tissue thickness; (4) bones corresponding to low to very low exposure levels (often overlapping with the foreground); and (5) background corresponding to very high exposure areas. These five classes of objects can be difficult to separate using the code value alone, because there can be considerable overlap between objects in different classes (such as with the bone and the collimator blades).
As has been noted in commonly assigned U.S. Pat. No. 5,633,511 entitled “Automatic Tone Scale Adjustment Using Image Activity Measures” to Lee et al., some basic problems in adjusting tone scale for computed radiography relate to: (1) determining which sub-range of the input code values is most important for clinical evaluation and (2) constructing a tone-scale transfer curve so that the important sub-range of the code values identified in step (1) can be rendered with proper contrast and brightness (density) on the output display or media. For example, the digital code values of an input chest x-ray image may span from 500 to 3000 (in units of 0.001 log exposure), but the code value range of the lung area, being the most important region of the image, may only span from about 1800 to 2600. Simply mapping the entire range of the input code value (from 500 to 3000) to the available film density range with equal contrast for all input code values can produce a chest image with an unacceptably low contrast, making it difficult to discern features clearly. It is, therefore, very useful to have an algorithm to automatically detect and select the relevant sub-range of the input code values (typically 1800 to 2600) to display on the output media with proper visual contrast and brightness. The process of selecting the relevant sub-range of input code values and constructing the proper mapping function from the input code value to the output display media is termed tone scale adjustment.
The Lee et al. '511 disclosure describes conventional approaches for identifying the sub-range of interest in the image, using a histogram of input code values, then discloses an improved alternative for identifying this sub-range, using an activity histogram. The activity histogram disclosed in the Lee et al. '511 patent gives a measure of line-by-line image activity that improves overall image rendering and has advantages for achieving improved image contrast and brightness.
Expanding upon the techniques of the Lee et al. '511 patent, a contrast enhancement method is also disclosed in commonly assigned U.S. Pat. No. 6,778,691 entitled “Method Of Automatically Determining Tone-Scale Parameters For A Digital Image” to Barski et al. The method of the Barski et al. '691 disclosure automatically generates a Look-Up Table (LUT) for obtaining a desired tone scale for an image, using the slope of the tone scale curve over its mid-range densities.
Conventional methods for adjusting the intensity range and slope of diagnostic image values may not provide satisfactory results in all cases. While methods such as those described in the Lee et al. '511 patent and in the Barski et al. '691 patent provide improvements in contrast enhancement for a diagnostic image, these methods do not address the problem of consistent rendering between images taken for a patient at different times or for images of different patients. Thus, for example, where two or more images for a patient taken at different times differ with respect to exposure values or other values, application of such contrast improvement techniques is not likely to provide consistent rendering that would allow more accurate assessment of condition changes by the ICU clinician.
Contrast stretching is one method that has been proposed for providing a measure of normalization between images. For example, U.S. Pat. No. 5,357,549 entitled “Method Of Dynamic Range Compression Of An X-Ray Image And Apparatus Effectuating The Method” to Maack et al. describes a dynamic range compression technique that stretches image intensity in only a particular area of interest, such as within the lung area of a chest X-ray. The proposed method locates low frequency components, determines equalization factors, and then applies these to the image for compressing low frequency components, freeing the remainder of the dynamic range for higher frequency areas of the image intensities. In a similar approach, U.S. Pat. No. 5,835,618 entitled “Uniform And Non-Uniform Dynamic Range Remapping For Optimum Image Display” to Fang uses a method of dynamic range remapping for enhancing the image in both dark and bright intensity areas. This remapping or correction technique amounts to smoothing the data (such as through a low-pass filter), determining the data mean, adjusting the smoothed data to the mean, and then applying smoothed, adjusted data to the original data. Methods such as those described above focus on improving the overall image appearance of individual images, which may in turn help to improve image consistency to some degree. However, these and other conventional contrast-stretching methods do not directly address inconsistency from image to image.
Thus, in spite of continuing attempts to achieve acceptable diagnostic quality of individual images, there remains considerable room for improvement in achieving an acceptable measure of consistency in diagnostic image rendering. The problem of providing consistency in image appearance is complicated by the number of different types of imaging systems that can be used, each having different preprocessing of the initial image data, by imaging techniques applied during the exam, and by viewer preferences for image content from different regions of interest. It would be beneficial to provide solutions to the rendering problem that provide consistent results for the same types of images taken over a period of time, such as for patients in an ICU or similar care facility.